Glioma Grading by Using Histogram Analysis of Blood Volume Heterogeneity from MR-derived Cerebral Blood Volume Maps

Purpose: To retrospectively compare the diagnostic accuracy of an alternative method used to grade gliomas that is based on histogram analysis of normalized cerebral blood volume (CBV) values from the entire tumor volume (obtained with the histogram method) with that of the hot-spot method, with histologic analysis as the reference standard.

Materials and Methods: The medical ethics committee approved this study, and all patients provided informed consent. Fifty-three patients (24 female, 29 male; mean age, 48 years; age range, 14–76 years) with histologically confirmed gliomas were examined with dynamic contrast material–enhanced 1.5-T magnetic resonance (MR) imaging. CBV maps were created and normalized to unaffected white matter (normalized CBV maps). Four neuroradiologists independently measured the distribution of whole-tumor normalized CBVs and analyzed this distribution by classifying the values into area-normalized bins. Glioma grading was performed by assessing the normalized peak height of the histogram distributions. Logistic regression analysis and interobserver agreement were used to compare the proposed method with a hot-spot method in which only the maximum normalized CBV was used.

Results: For the histogram method, diagnostic accuracy was independent of the observer. Interobserver agreement was almost perfect for the histogram method (κ = 0.923) and moderate for the hot-spot method (κ = 0.559). For all observers, sensitivity was higher with the histogram method (90%) than with the hot-spot method (55%–76%).

Conclusion: Glioma grading based on histogram analysis of normalized CBV heterogeneity is an alternative to the established hot-spot method, as it offers increased diagnostic accuracy and interobserver agreement.

Supplemental material: http://radiology.rsnajnls.org/cgi/content/full/247/3/808/DC1

© RSNA, 2008

References

  • 1 Lev MH, Rosen BR. Clinical applications of intracranial perfusion MR imaging. Neuroimaging Clin N Am 1999; 9(2): 309–331. MedlineGoogle Scholar
  • 2 Law M, Oh S, Babb JS, et al. Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging—prediction of patient clinical response. Radiology 2006;238(2):658–667. LinkGoogle Scholar
  • 3 Covarrubias DJ, Rosen BR, Lev MH. Dynamic magnetic resonance perfusion imaging of brain tumors. Oncologist 2004;9(5):528–537. Crossref, MedlineGoogle Scholar
  • 4 Edelman RR, Mattle HP, Atkinson DJ, et al. Cerebral blood flow: assessment with dynamic contrast-enhanced T2*-weighted MR imaging at 1.5 T. Radiology 1990;176(1):211–220. LinkGoogle Scholar
  • 5 Aronen HJ, Gazit IE, Louis DN, et al. Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 1994;191(1):41–51. LinkGoogle Scholar
  • 6 Knopp EA, Cha S, Johnson G, et al. Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. Radiology 1999;211(3):791–798. LinkGoogle Scholar
  • 7 Lev MH, Ozsunar Y, Henson JW, et al. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas. AJNR Am J Neuroradiol 2004;25(2):214–221. MedlineGoogle Scholar
  • 8 Kleihues P, Cavenee WK. Astrocytic tumors and oligodendroglial tumors and mixed gliomas. In: Kleihues P, Cavenee WK, eds. The WHO classification of tumors of the nervous system. Lyon, France: International Agency for Research on Cancer, 2000; 9–70. Google Scholar
  • 9 Rosen BR, Belliveau JW, Vevea JM, Brady TJ. Perfusion imaging with NMR contrast agents. Magn Reson Med 1990;14(2):249–265. Crossref, MedlineGoogle Scholar
  • 10 Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. I. Mathematical approach and statistical analysis. Magn Reson Med 1996;36(5):715–725. Crossref, MedlineGoogle Scholar
  • 11 Boxerman JL, Schmainda KM, Weisskoff RM. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 2006;27(4):859–867. MedlineGoogle Scholar
  • 12 Wetzel SG, Cha S, Johnson G, et al. Relative cerebral blood volume measurements in intracranial mass lesions: interobserver and intraobserver reproducibility study. Radiology 2002;224(3):797–803. LinkGoogle Scholar
  • 13 Bjornerud A. The ICE software package: direct co-registration of anatomical and functional datasets using DICOM image geometry information. Proc Hum Brain Mapping 2003;19(2):1018p. Google Scholar
  • 14 Schmainda KM, Rand SD, Joseph AM, et al. Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. AJNR Am J Neuroradiol 2004;25(9):1524–1532. MedlineGoogle Scholar
  • 15 Law M, Yang S, Wang H, et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 2003;24(10):1989–1998. MedlineGoogle Scholar
  • 16 Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33(1):159–174. Crossref, MedlineGoogle Scholar
  • 17 Fryback DG, Thornbury JR. The efficacy of diagnostic imaging. Med Decis Making 1991;11(2):88–94. Crossref, MedlineGoogle Scholar
  • 18 Sugahara T, Korogi Y, Kochi M, et al. Correlation of MR imaging-determined cerebral blood volume maps with histologic and angiographic determination of vascularity of gliomas. AJR Am J Roentgenol 1998;171(6):1479–1486. Crossref, MedlineGoogle Scholar
  • 19 Hakyemez B, Erdogan C, Ercan I, Ergin N, Uysal S, Atahan S. High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol 2005;60(4):493–502. Crossref, MedlineGoogle Scholar
  • 20 Claus EB, Black PM. Survival rates and patterns of care for patients diagnosed with supratentorial low-grade gliomas: data from the SEER program, 1973–2001. Cancer 2006;106(6):1358–1363. Crossref, MedlineGoogle Scholar
  • 21 Nelson JS, Tsukada Y, Schoenfeld D, Fulling K, Lamarche J, Peress N. Necrosis as a prognostic criterion in malignant supratentorial, astrocytic gliomas. Cancer 1983;52(3):550–554. Crossref, MedlineGoogle Scholar
  • 22 Donahue KM, Krouwer HG, Rand SD, et al. Utility of simultaneously acquired gradient-echo and spin-echo cerebral blood volume and morphology maps in brain tumor patients. Magn Reson Med 2000;43(6):845–853. Crossref, MedlineGoogle Scholar
  • 23 Sugahara T, Korogi Y, Kochi M, Ushio Y, Takahashi M. Perfusion-sensitive MR imaging of gliomas: comparison between gradient-echo and spin-echo echo-planar imaging techniques. AJNR Am J Neuroradiol 2001;22(7):1306–1315. MedlineGoogle Scholar
  • 24 Aronen HJ, Perkio J. Dynamic susceptibility contrast MRI of gliomas. Neuroimaging Clin N Am 2002;12(4):501–523. Crossref, MedlineGoogle Scholar
  • 25 Price SJ, Jena R, Burnet NG, et al. Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol 2006;27(9):1969–1974. MedlineGoogle Scholar
  • 26 Grier JT, Batchelor T. Low-grade gliomas in adults. Oncologist 2006;11(6):681–693. Crossref, MedlineGoogle Scholar
  • 27 Itskovich VV, Samber DD, Mani V, et al. Quantification of human atherosclerotic plaques using spatially enhanced cluster analysis of multicontrast-weighted magnetic resonance images. Magn Reson Med 2004;52(3):515–523. Crossref, MedlineGoogle Scholar

Article History

Published in print: 2008